Essential tremor (ET) is the most common movement disorder in the United States, affecting 4% of all adults over the age of 40. For individuals whose motor symptoms are refractory to medication and significantly impair their daily living, deep brain stimulation (DBS) is considered to be the only therapeutic option. Despite recent advances in DBS technology, a significant portion of ET patients with DBS implants will receive inadequate tremor control because of poorly placed DBS leads, while others will lose efficacy of the therapy after 1-2 years due in part to inflexible neurostimulator programming options. There is a strong and growing clinical need for implantable DBS lead designs that can enable clinicians to better sculpt electric fields within the brain, especially in cases where stimulation through a poorly placed DBS lead results in low-threshold side-effects. Our recent studies with a radially-segmented DBS lead have shown promising results, but knowing how to program the stimulation settings on such a lead remains a critical challenge towards making these leads practical in a clinical setting. Our proposed study will integrate high-field magnetic resonance imaging, computational modeling, and electrophysiology to develop an experimentally-validated computational programming algorithm that facilitates clinical determination of subject-specific neurostimulator settings through high-dimensional DBS electrode arrays. Specifically, we will: 1) develop a computational algorithm that can simplify the programming process of thalamic deep brain stimulation leads with radially-segmented electrode arrays; 2) quantify the degree to which the computational algorithms can accurately predict current steering through poorly targeted DBS arrays in the thalamus in non-human primates; and 3) compare the layer-specific neuronal dynamics induced in primary motor cortex (M1) during stimulation of the cerebellothalamic versus thalamocortical pathway in non-human primates.